Mohammadali Olyaei, Ardeshir Ebtehaj, Christopher R Ellis
{"title":"A Hyperspectral Reflectance Database of Plastic Debris with Different Fractional Abundance in River Systems.","authors":"Mohammadali Olyaei, Ardeshir Ebtehaj, Christopher R Ellis","doi":"10.1038/s41597-024-03974-x","DOIUrl":null,"url":null,"abstract":"<p><p>Plastic debris pollution transported by river systems to lakes and oceans has emerged as a significant environmental concern with adverse impacts on ecosystems, food webs, and human health. Remote sensing presents a cost-effective approach to bolster interception and removal efforts. However, unlike marine environments, the optical properties of plastic debris in fresh waters remain poorly understood. This study aims to address this gap by providing an open-access hyperspectral reflectance database of floating weathered and virgin plastic debris found in river systems under controlled laboratory experiments. Utilizing natural waters from the Mississippi River, the database was assembled using a remote sensing data acquisition system deployed over a hydraulic flume operating under subcritical flow conditions and varying suspended sediment concentrations. The measurements encompass hyperspectral diffused light reflectance from ultraviolet (UV, 350 nm) to shortwave infrared (SWIR, 2500 nm) wavelengths. The database archived in Network Common Data Form (NetCDF) and Comma-separated values (CSV), offers valuable insights for better understanding key spectral signatures indicative of floating plastic debris, with different fractional abundance, in freshwater ecosystems.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1253"},"PeriodicalIF":5.8000,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579464/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-024-03974-x","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Plastic debris pollution transported by river systems to lakes and oceans has emerged as a significant environmental concern with adverse impacts on ecosystems, food webs, and human health. Remote sensing presents a cost-effective approach to bolster interception and removal efforts. However, unlike marine environments, the optical properties of plastic debris in fresh waters remain poorly understood. This study aims to address this gap by providing an open-access hyperspectral reflectance database of floating weathered and virgin plastic debris found in river systems under controlled laboratory experiments. Utilizing natural waters from the Mississippi River, the database was assembled using a remote sensing data acquisition system deployed over a hydraulic flume operating under subcritical flow conditions and varying suspended sediment concentrations. The measurements encompass hyperspectral diffused light reflectance from ultraviolet (UV, 350 nm) to shortwave infrared (SWIR, 2500 nm) wavelengths. The database archived in Network Common Data Form (NetCDF) and Comma-separated values (CSV), offers valuable insights for better understanding key spectral signatures indicative of floating plastic debris, with different fractional abundance, in freshwater ecosystems.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.